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About: Orthogonality principle     Goto   Sponge   NotDistinct   Permalink

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Type: yago:WikicatStatisticalPrinciplesyago:Abstraction100002137yago:Cognition100023271yago:Content105809192yago:Generalization105913275yago:Idea105833840yago:Principle105913538yago:PsychologicalFeature100023100disease New Facet based on Instances of this Class

In statistics and signal processing, the orthogonality principle is a necessary and sufficient condition for the optimality of a Bayesian estimator. Loosely stated, the orthogonality principle says that the error vector of the optimal estimator (in a mean square error sense) is orthogonal to any possible estimator. The orthogonality principle is most commonly stated for linear estimators, but more general formulations are possible. Since the principle is a necessary and sufficient condition for optimality, it can be used to find the minimum mean square error estimator.